稳健性(进化)
计算机科学
贝叶斯概率
噪音(视频)
振幅
信号处理
算法
贝叶斯推理
领域(数学)
人工智能
机器学习
数学
物理
电信
纯数学
化学
雷达
图像(数学)
基因
量子力学
生物化学
作者
Kay L. Gemba,Santosh Nannuru,Peter Gerstoft
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2019-10-01
卷期号:146 (4_Supplement): 2927-2927
摘要
Ed Sullivan’s legacy includes significant contributions to the field of signal processing. Inspired by his Bayesian approach, we present results for a method coined Sparse Bayesian learning (SBL) to estimate source parameters. Previously, SBL has been applied to the matched field processing application [K. L. Gemba, S. Nannuru, and P. Gerstoft, “Robust ocean acoustic localization with sparse Bayesian learning,” IEEE J. Sel. Top. Signal Process. 13(1), 49–60 (2019)]. This multi-source scenario required adaptive and robust processing, and included a non-stationary noise model. The adaptive SBL algorithm models the complex source amplitudes as random quantities, providing a degree of robustness to amplitude and phase errors. Further, its formulation is flexible and can accommodate advanced noise models. We consider the application of different noise models in simulations and experimental data and compare SBL performance to traditional processing.
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